Disambiguation of Concept Classifications Using Language-Specific Clues

ABSTRACT

A computer-implemented method, system, and computer program product for classifying a concept of a data item. A data item label for the data item is received. The data item label is analyzed using a natural language processing tool to generate additional lexical information for the data item label. A search query is built using the additional lexical information for the data item label. The search query is used to search a clue concept index to produce a search result. The clue concept index comprises clue concept records that identify clues for concepts. The search result identifies clue concept records from the clue concept index that match the search query. A concept is selected from the clue concept records identified in the search result as the concept for the data item.

BACKGROUND 1. Field

The disclosure relates generally to modeling data for businessintelligence and other applications. More particularly, illustrativeembodiments relate to a computer implemented method, a system, and acomputer program product for identifying the concepts of data usinglanguage specific clues. More particularly, illustrative embodimentsrelate to a computer implemented method, a system, and a computerprogram product for generating a semantic model of data using bothsemantic concepts in the data and characteristics of the data.

2. Description of the Related Art

Business enterprises and other organizations generate large amounts ofdata every day. This data may range from fully structured enterprisedata sources, such as databases or multidimensional data cubes, tosemi-structured sources, such as comma-separated values, CSV, files orelectronic spreadsheets. To help improve business performance and drivecompetitive advantage, users expect that artificial intelligence andanalytics systems are able to understand the data, quickly find hiddenpatterns in the data, discover insights to help them make informeddecisions faster, or recommend solutions to help them solve complexproblems.

An important building block to achieve these goals and satisfy the needsof enterprise customers is to understand the semantic meaning of theirdata, discover underlying relationships among these data, and capturethe knowledge discovered from the data in a conceptual or semantic modelthat represents the business interpretation of the data. A semanticmodel should be able to describe the structure of the data, semanticmeanings and data characteristics of each data item, relationshipsbetween data items, groups and hierarchies among data items, and more.

Therefore, it would be desirable to have a method and apparatus thattake into account at least some of the issues discussed above, as wellas other possible issues. For example, it would be desirable to have amethod and apparatus that overcome technical problems with classifyingthe concepts of data using language clues in the data.

SUMMARY

According to illustrative embodiments, a computer-implemented method ofclassifying a concept of a data item is provided. A data item label isreceived for the data item. The data item label is analyzed using anatural language processing tool to generate additional lexicalinformation for the data item label. A search query is built using theadditional lexical information for the data item label. The search queryis used to search a clue concept index to produce a search result. Theclue concept index comprises clue concept records that identify cluesfor concepts. The search result identifies clue concept records from theclue concept index that match the search query. A concept from the clueconcept records identified in the search result is selected as theconcept for the data item.

According to illustrative embodiments, a system for classifying aconcept of a data item is provided. The system comprises a dataprocessing system that is configured to receive a data item label forthe data item, analyze the data item label using a natural languageprocessing tool to generate additional lexical information for the dataitem label, build a search query using the additional lexicalinformation for the data item label, and use the search query to searcha clue concept index to produce a search result. The clue concept indexcomprises clue concept records that identify clues for concepts. Thesearch result identifies clue concept records from the clue conceptindex that match the search query. The data processing system isconfigured to select a concept from the clue concept records identifiedin the search result as the concept for the data item.

According to illustrative embodiments, a computer program product forclassifying a concept of a data item is provided. The computer programproduct comprises a computer readable storage medium having programinstructions embodied therewith. The program instructions are executableby a device to cause the device to receive a data item label for thedata item, analyze the data item label using a natural languageprocessing tool to generate additional lexical information for the dataitem label, build a search query using the additional lexicalinformation for the data item label, and use the search query to searcha clue concept index to produce a search result. The clue concept indexcomprises clue concept records that identify clues for concepts. Thesearch result identifies clue concept records from the clue conceptindex that match the search query. The program instructions areexecutable by the device to cause the device to select a concept fromthe clue concept records identified in the search result as the conceptfor the data item.

Other variations are possible, as described below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments may be implemented;

FIG. 2 is a block diagram of an enterprise system including a semanticmodel generator in accordance with an illustrative embodiment;

FIG. 3 is a block diagram of a clue concept index builder in accordancewith an illustrative embodiment;

FIG. 4 is a block diagram of a concept classifier in accordance with anillustrative embodiment;

FIG. 5 is an illustration of a flowchart of a process for the conceptclassification of data in accordance with an illustrative embodiment;

FIG. 6 is an illustration of a flowchart of a process for generating aclue concept index in accordance with an illustrative embodiment

FIG. 7 is an illustration of a flowchart of a process for using a clueconcept index for the concept classification of data in accordance withan illustrative embodiment; and

FIG. 8 is a block diagram of a data processing system in accordance withan illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments recognize and take into account that,traditionally, an artificial intelligence and analytics system mayrequire a data modeler to describe the data manually. Such a manualsolution is time consuming and requires intensive training to the datamodelers.

In recent years, artificial intelligence and analytics tools and systemshave used different approaches to automatically generate semantic modelsto serve the demands of users. However, current systems and methods arenot able to disambiguate semantic meanings automatically. For example,for data item SALES REP, current approaches could not automaticallydisambiguate if the field is mostly about REVENUE or about PEOPLE. Lackof accuracy in semantic models that describe the semantic meaning ofcustomer data may result in irrelevant or wrong recommendations andsignificantly damage credibility and trust that can foster userdissatisfaction or abandonment of analytic systems.

Illustrative embodiments provide a method and apparatus to automaticallydisambiguate concept classifications using language-specific clues. Withthis method, an artificial intelligence and analytics system canunderstand the semantic meaning of customer data better, automaticallybuild a semantic model that describes data with more accurate conceptsand discover more accurate relationships, and capture deep knowledge tobe used by recommenders.

Illustrative embodiments disambiguate and improve the accuracy ofconcept classifications via a set of operations, including automaticallygenerating and curating lexical clues, analyzing and enriching clues anddata item labels with additional lexical information, includingsignificance for each token, creating unique language-specific conceptclue indices, searching clues using a boosted query built with data itemlabel lexical information, and computing a weighted relevance score witha custom scoring algorithm.

Illustrative embodiments provide a system, method, and computer programproduct for disambiguation in concept classification which solves theambiguity issues that may be caused by previous methods. Illustrativeembodiments also provide a relatively simple and natural way for a userto provide extra language-specific clues to improve conceptclassification and eliminate unnecessary clues for concept class labels.Illustrative embodiments may be used to build a much more accuratesemantic model automatically, without user intervention. Therefore,artificial intelligence and analytics systems and methods that usesemantic models that are generated in accordance with illustrativeembodiments to accurately describe customer data are better able toautomatically recommend solutions to help business users and others tosolve complex problems and make informed decisions faster.

With reference now to the figures and, in particular, with reference toFIG. 1, a pictorial representation of a network of data processingsystems is depicted in which illustrative embodiments may beimplemented. Network data processing system 100 is a network ofcomputers in which the illustrative embodiments may be implemented.Network data processing system 100 contains network 102, which is themedium used to provide communications links between various devices andcomputers connected together within network data processing system 100.Network 102 may include connections, such as wire, wirelesscommunication links, or fiber optic cables.

In the depicted example, server computer 104 and server computer 106connect to network 102 along with storage unit 108. In addition, clientcomputer 110, client computer 112, and client computer 114 connect tonetwork 102. Client computers 110, 112, and 114 can be, for example,computers, workstations, or network computers. In the depicted example,server computer 104 provides information, such as boot files, operatingsystem images, and applications to client computers 110, 112, and 114.In this illustrative example, server computer 104, server computer 106,storage unit 108, and client computers 110, 112, and 114 are networkdevices that connect to network 102 in which network 102 is thecommunications media for these network devices.

Client computers 110, 112, and 114 are clients to server computer 104 inthis example. Network data processing system 100 may include additionalserver computers, client computers, and other devices not shown. Clientcomputers 110, 112, and 114 connect to network 102 utilizing at leastone of wired, optical fiber, or wireless connections.

Program code located in network data processing system 100 can be storedon a computer-recordable storage medium and downloaded to a dataprocessing system or other device for use. For example, program code canbe stored on a computer-recordable storage medium on server computer 104and downloaded to client computers 110, 112, or 114 over network 102 foruse on client devices 110, 112, or 114.

In the depicted example, network data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers consisting of thousands of commercial, governmental,educational, and other computer systems that route data and messages. Ofcourse, network data processing system 100 also may be implemented usinga number of different types of networks. For example, network 102 can becomprised of at least one of the Internet, an intranet, a local areanetwork (LAN), a metropolitan area network (MAN), or a wide area network(WAN). FIG. 1 is intended as an example, and not as an architecturallimitation for the different illustrative embodiments.

As used herein, “a number of” when used with reference to items, meansone or more items. For example, “a number of different types ofnetworks” is one or more different types of networks.

The phrase “at least one of,” when used with a list of items, meansdifferent combinations of one or more of the listed items can be used,and only one of each item in the list may be needed. In other words, “atleast one of” means any combination of items and number of items may beused from the list, but not all of the items in the list are required.The item can be a particular object, a thing, or a category.

For example, without limitation, “at least one of item A, item B, oritem C” may include item A, item A and item B, or item B. This examplealso may include item A, item B, and item C or item B and item C. Ofcourse, any combinations of these items can be present. In someillustrative examples, “at least one of” can be, for example, withoutlimitation, two of item A; one of item B; and ten of item C; four ofitem B and seven of item C; or other suitable combinations.

With reference to FIG. 2, a block diagram of an enterprise systemincluding a semantic model generator is depicted in accordance with anillustrative embodiment. In this illustrative example, enterprise system200 includes components that may be implemented in hardware such as thehardware shown in network data processing system 100 in FIG. 1.

Enterprise system 200 may be implemented in software, hardware, firmwareor a combination thereof. When software is used, the operationsperformed by enterprise system 200 may be implemented in program codeconfigured to run on hardware, such as a processor unit. When firmwareis used, the operations performed by enterprise system 200 may beimplemented in program code and data and stored in persistent memory torun on a processor unit. When hardware is employed, the hardware mayinclude circuits that operate to perform the operations in enterprisesystem 200.

In the illustrative examples, the hardware may take a form selected fromat least one of a circuit system, an integrated circuit, an applicationspecific integrated circuit (ASIC), a programmable logic device, or someother suitable type of hardware configured to perform a number ofoperations. With a programmable logic device, the device can beconfigured to perform the number of operations. The device can bereconfigured at a later time or can be permanently configured to performthe number of operations. Programmable logic devices include, forexample, a programmable logic array, a programmable array logic, a fieldprogrammable logic array, a field programmable gate array, and othersuitable hardware devices. Additionally, the processes can beimplemented in organic components integrated with inorganic componentsand can be comprised entirely of organic components excluding a humanbeing. For example, the processes can be implemented as circuits inorganic semiconductors.

Enterprise system 200 may be operated by or for any appropriateorganization 202. For example, without limitation, organization 202 maybe business enterprise 204 or other organization 206. Organization 202may perform business or other appropriate operations in domain 208.

During operations, and otherwise, enterprise system 200 may generatedata 210 from various data sources 212. For example, without limitation,data sources 212 may include fully-structured data source 214,semi-structured data source 216, other data source 218, or any otherappropriate combination of data sources. Examples of fully-structureddata source 214 may include database 220 and multidimensional data cube222. Examples of semi-structured data source 216 may include electronicspreadsheet 224 and comma-separated values, CSV, file 226.

Data 212 may comprise a plurality of data items 228. Each data item 230in plurality of data items 228 may comprise data item label 232 and datavalues 234. Data item label 232 may comprise alphanumeric text, such asa word, phrase, abbreviation of a word or a phrase, code, or symbolwhich describes what data values 234 of data item 230 refer to. Datavalues 234 in data item 230 may have various data characteristics 236.Data characteristics 236 may comprise any appropriate characteristic ofdata values 234 in data item 230.

Enterprise system 200 may include semantic model generator 238. Inaccordance with an illustrative embodiment, semantic model generator 238is configured to automatically generate semantic model 240 of data 210using a holistic approach by representing the knowledge discovered fromdata 210 not only with more accurate semantic concepts in domain 208 ofdata 210 but also with a set of data concepts that represent datacharacteristics using ontological methods independently from semanticconcepts that are tied to a particular domain 208. Alternatively, someor all of the functions performed by semantic model generator 238 may beperformed outside of enterprise system 200 with resulting semantic model240 provided to enterprise system 200 for use by organization 202.

Enterprise system 200 may use semantic model 240 of data 210 to performanalytics 242. Analytics 242 may include the discovery, interpretation,and communication of meaningful patterns in data 210 and the process ofapplying those patterns towards effective decision making. For example,without limitation, organization 202 may apply analytics to data 210using semantic model 240 to describe, diagnose, predict, and improvebusiness performance. Semantic model 240 may be used to perform anyappropriate analytics 242.

Analytics 242 may be performed using any appropriate analytics tool 244or analytics system 246. Analytics tool 244 or analytics system 246 maybe implemented as part of enterprise system 200. Alternatively, some orall of the functions performed by analytics tool 244 or analytics system246 may be performed outside of enterprise system 200 with the resultsof analytics 242 provided to enterprise system 200 for use byorganization 202.

For example, without limitation, analytics system 246 may comprisebusiness intelligence system 248. Business intelligence system 248 maybe configured to perform analytics 242 using semantic model 240 of data210 to improve business performance of business enterprise 204 or otherorganization 206. Analytics tool 244 and analytics system 246 may useartificial intelligence 250 to perform analytics 242. For example,analytics 242 may include generating visualizations 252 of data 210using semantic model 240 of data 210. Visualizations 252 may include thegraphic representation of data 210 including images that communicaterelationships among the represented data to viewers of the images.

The illustration of enterprise system 200 in FIG. 2 is not meant toimply physical or architectural limitations to the manner in which anillustrative embodiment can be implemented. Other components in additionto or in place of the ones illustrated may be used. Some components maybe unnecessary. Also, the blocks are presented to illustrate somefunctional components. One or more of these blocks may be combined,divided, or combined and divided into different blocks when implementedin an illustrative embodiment.

Turning to FIG. 3, a block diagram of a clue concept index builder isdepicted in accordance with an illustrative embodiment. Clue conceptindex builder 300 may be implemented in enterprise system 200 in FIG. 2.

Clue concept index builder 300 is configured to generate clue conceptindex 302 for concepts 304. Concepts 304 may be identified from semanticconcept ontology 306. Semantic concept ontology 306 may bedomain-specific 308. Lexical information 310 for concepts 304 may beidentified in semantic concept ontology 306.

Clue concept index builder 300 may include lexical clue generator 312.Lexical clue generator 312 may be configured to automatically generatelexical clues 314 for concepts 304 from concept labels 316 for concepts304. Lexical clues 314 for concepts 304 also may include extra lexicalclues 318. Extra lexical clues 318 may be manually generated 320.

Clue concept index builder 300 is configured to use natural languageprocessing tool 322 to generate additional lexical information 324 forconcepts 304 from lexical clues 314. For example, without limitation,additional lexical information 324 may include tokens 326. Each token328 in tokens 326 may include text 330, lemma 332 for text 330, anindication of significance 334, and other information 336. For example,text 330 may be a portion of the text from one of lexical clues 314.

For example, lexical clue “weekly hour worked” may be processed usingnatural language processing tool 322 to generate the followingadditional lexical information 324:

{ “text”: “weekly hour worked”, “tokens”: [{ “text”: “weekly”, “lemma”:“weekly”, “significance”: 5, “pos”: “JJ”, “positionInPhrase” : 1 }, {“text”: “hour”, “lemma”: “hour”, “significance”: 8, “pos”: “NN”,“positionInPhrase”: 2 }, { “text”: “worked”, “lemma”: “work”,“significance″: 5, “pos”: “VBD”, “positionInPhrase”: 3 } ] }.

Clue concept index builder 300 may be configured to generate clueconcept records 338 using each clue information and additional lexicalinformation 324. Clue concept records associate concepts 304 with clues340 derived from clue information and additional lexical information324.

An example of a clue concept record generated in accordance with anillustrative embodiment using the additional lexical information 324 for“weekly hour work” presented above is:

{ “id”: “9841900d|85877537|dffdkda0”, “text”: “weekly hour worked”,“textTokenized”: “weekly hour worked”, “textLemma″: “weekly hour workedwork″, “textLexicalInfo”: “{\“text\”:\”weekly hour worked\”, \“tokens\”: [{\“text\”:\“weekly\”,\“significance\”:

,\“pos\”:“JJ\”,\“

\”:\“weekly\ ”,\“positionInPhrase\”:,},{\“text\”:\“hour\”,\“significance\”: ,\“pos\”:\“NN\”,\“

\”:\“hour\”,\“positionInPhrase\”:2;,{\“text\”:\“worked\”,\“significance\”:8,\“pos\”:\“VBD\”,\“

\”:\ “work\”,\“positionInPhrase\”:3}]”, “conceptID”: “http://www.

.com/ontologies/

/domain/common

Duration”. “ontologyID”: “http://www.

.com/ontologies/

/domain/common” },

indicates data missing or illegible when filed

Clue concept index builder 300 may be configured to incorporate clueconcept records 338 into clue concept index 302 to form clue conceptindex 302.

Clue concept index 302 may be language-specific 342. For example,without limitation, clue concept index 302 may include English clueconcept index 344, French clue concept index 346, Spanish clue conceptindex 348, or other language-specific clue concept index 350.

Turning to FIG. 4, a block diagram of a concept classifier is depictedin accordance with an illustrative embodiment. Concept classifier 400may be implemented in enterprise system 200 in FIG. 2.

Concept classifier 400 is configured to identify concepts 402 for dataitem 404. Concept classifier 400 may include natural language processingtool 406, search query builder 408, index searcher 410, relevance scorebooster 412, relevance score weighter 414, clue concept record ranker416, and concept selector 418.

Concept classifier 400 may be configured to receive data item label 420for data item 404 to be classified. Natural language processing tool 406is configured to analyze data item label 420 to generate additionallexical information 422. Additional lexical information 422 may includetokens 424 for data item label 420. Each token 426 in tokens 424 mayinclude text 428, lemma 430 for text 428, an indication of significance432, or other information 434.

Search query builder 408 is configured to generate search query 436using additional lexical information 402 for data item label 420. Forexample, without limitation, search query 436 may use text 428 and lemma430 from each token 426 of data item label 420.

Index searcher 410 is configured to use search query 436 to search clueconcept indexes 438 to obtain search result 440. Clue concept index 302generated by clue concept index builder 300 in FIG. 3 is an example ofclue concept indexes 438 that may be searched using search query 436.For example, without limitation, if the language of data item 404 isdetermined to be a language other than English, search query 436 may beused to search a language-specific clue concept index for the languageof data item 404 and an English clue concept index. If the language ofdata item 404 is determined to be English, search query 436 may be usedto search only an English clue concept index.

Search result 440 may include matched clue concept records 442 andcorresponding relevance scores 444 for matched clue concept records 442.For example, matched clue concept records 442 may be clue conceptrecords from clue concept indexes 438 that matched the terms in searchquery 436. Relevance scores 444 may indicate how well or closely matchedclue concept records 442 matched the terms in search query 436.

Relevance score booster 412 is configured to modify relevance scores 444to generate boosted relevance scores 446. Relevance score booster 412may be configured to increase or decrease relevance scores 444 based onwhich fields in matched clue concept records 442 were matched to searchquery 436 in the search. For example, without limitation, if theoriginal lexical clue text was matched, the relevance score for thecorresponding matched clue concept record may be multiplied by 1.5. Ifthe original lexical clue text was not matched, but the text of a tokenwas matched, the relevance score for the corresponding matched clueconcept record may be multiplied by 1.2. If the original lexical cluetext and the token text were not matched, but the lemma of a token wasmatched, the relevance score for the corresponding matched clue conceptrecord may not be increased. In this last example, the boosted relevancescore for the clue concept record would be the same as the relevancescore for the clue concept record.

Relevance score weighter 414 may be configured to apply weights 448 toboosted relevance scores 446 to generate weighted relevance scores 450.For example, without limitation, relevance score weighter 414 may beconfigured to generate weighted relevance scores 450 by factoringboosted relevance scores 446 with weights 448 calculated by scoringalgorithm 452. Relevance score weighter 414 may add weights 448 to eachpair of matched items based on their significance and sources, from dataitem label or from clue, when calculating a ratio of matched itemsversus total items. Weights 448 calculated by scoring algorithm 452 maybe trained by machine learning model 454.

Clue concept record ranker 416 is configured to rank matched clueconcept records 442 in search result 440 by weighted relevance scores450 for matched clue concept records 442 to provide ranked clue conceptrecords 456. Concept selector 418 may be configured to select conceptsfrom the highest ranked clue concept record in ranked clue conceptrecords 456 as concepts 402 for data item 404. In other words, conceptselector 418 is configured to select concepts from the clue conceptrecord with the highest weighted relevance score as concepts 402identified for data item 404.

Turning to FIG. 5, an illustration of a flowchart of a process for theconcept classification of data is depicted in accordance with anillustrative embodiment. Process 500 may be implemented in enterprisesystem 200 to identify concepts in data 210 in FIG. 2.

Process 500 may begin with generating a clue concept index (operation502). The clue concept index may identify various clues for variousconcepts that may be identified in data. In accordance with anillustrative embodiment, the clues for concepts in the clue conceptindex may be automatically generated and enhanced using a naturallanguage processing tool. The clue concept index then may be used forthe concept classification of data (operation 504), with the processterminating thereafter. For example, the clue concept index may besearched using a search query based on a data item label for a data itemto identify concepts for the data item. In accordance with anillustrative embodiment, the search query may include lexicalinformation that is generated by processing the data item label using anatural language processing tool.

Turning to FIG. 6, an illustration of a flowchart of a process forgenerating a clue concept index is depicted in accordance with anillustrative embodiment. For example, without limitation, process 600may be performed by clue concept index builder 300 in FIG. 3. Process600 may be an example of one implementation of operation 502 in process500 in FIG. 5.

Process 500 may begin with receiving concept labels for various concepts(operation 502). The concept labels may comprise names, aliases, words,or other information that describes or signifies the concepts. Lexicalclues may be automatically generated from the concept labels (operation504). Lexical clues also may be generated manually by a human operator(operation 506). A natural language processing tool then may be used toanalyze and enrich each of the lexical clues with additional lexicalinformation such as tokes, lemma, and an indication of significance foreach token (operation 508). Clue concept records then may be createdusing the lexical clues and corresponding additional lexical information(operation 510). The clue concept records may identify the lexical cluesand additional lexical information that are clues for specific concepts.The clue concept records may be incorporated into a clue concept index(operation 512), with the process terminating thereafter.

Turning to FIG. 7, an illustration of a flowchart of a process for usinga clue concept index for the concept classification of data is depictedin accordance with an illustrative embodiment. For example, withoutlimitation, process 700 may be performed by concept classifier 400 inFIG. 4. Process 700 may be an example of one implementation of operation504 in process 500 in FIG. 5.

Process 700 may begin with receiving a data item label for a data itemto be classified (operation 702). A natural language processing tool maybe used to process the data item label to generate additional lexicalinformation (operation 704). For example, without limitation, theadditional lexical information may include tokens, lemma, and anindication of significance for each token. A search query then may bebuilt using the additional lexical information (operation 706). Forexample, without limitation, the search query may be built to includethe text and lemma from each token of the data item label as searchterms. The search query then may be used to search a clue concept indexto produce a search result identifying matched clue concept records thatmatch the search query and corresponding relevance scores for thematched clue concept records (operation 708). Each of the relevancescores may indicate, for example, without limitation, how well thesearch query matched a particular corresponding clue concept record inthe search.

The relevance scores may be increased based on which field in the clueconcept record was matched in the search to generate boosted relevancescores for the matched clue concept records (operation 710). A weightedrelevance score may be generated for each matched clue concept record inthe search result by weighting the boosted relevance score for the clueconcept record with a weight determined by a scoring algorithm(operation 712). The matched clue concept records from the search resultthen may be ranked based on the weighted relevance scores for the clueconcept records (operation 714). Concepts from the highest ranked clueconcept record may be selected as the concepts for the data item(operation 716), with the process terminating thereafter.

The flowcharts and block diagrams in the different depicted embodimentsillustrate the architecture, functionality, and operation of somepossible implementations of apparatuses and methods in an illustrativeembodiment. In this regard, each block in the flowcharts or blockdiagrams may represent at least one of a module, a segment, a function,or a portion of an operation or step. For example, one or more of theblocks can be implemented as program code, hardware, or a combination ofthe program code and hardware. When implemented in hardware, thehardware may, for example, take the form of integrated circuits that aremanufactured or configured to perform one or more operations in theflowcharts or block diagrams. When implemented as a combination ofprogram code and hardware, the implementation may take the form offirmware. Each block in the flowcharts or the block diagrams can beimplemented using special purpose hardware systems that perform thedifferent operations or combinations of special purpose hardware andprogram code run by the special purpose hardware.

In some alternative implementations of an illustrative embodiment, thefunction or functions noted in the blocks may occur out of the ordernoted in the figures. For example, in some cases, two blocks shown insuccession can be performed substantially concurrently, or the blocksmay sometimes be performed in the reverse order, depending upon thefunctionality involved. Also, other blocks can be added in addition tothe illustrated blocks in a flowchart or block diagram.

Turning to FIG. 8, a block diagram of a data processing system isdepicted in accordance with an illustrative embodiment. Data processingsystem 800 can be used to implement server computer 104, server computer106, client computer 110, client computer 112, and client computer 114in FIG. 1. Data processing system 800 can also be used to implemententerprise system 200 in FIG. 2. In this illustrative example, dataprocessing system 800 includes communications framework 802, whichprovides communications between processor unit 804, memory 806,persistent storage 808, communications unit 810, input/output (I/O) unit812, and display 814. In this example, communications framework 802takes the form of a bus system.

Processor unit 804 serves to execute instructions for software that canbe loaded into memory 806. Processor unit 804 includes one or moreprocessors. For example, processor unit 804 can be selected from atleast one of a multicore processor, a central processing unit (CPU), agraphics processing unit (GPU), a physics processing unit (PPU), adigital signal processor (DSP), a network processor, or some othersuitable type of processor. For example, further, processor unit 804 canmay be implemented using one or more heterogeneous processor systems inwhich a main processor is present with secondary processors on a singlechip. As another illustrative example, processor unit 804 can be asymmetric multi-processor system containing multiple processors of thesame type on a single chip.

Memory 806 and persistent storage 808 are examples of storage devices816. A storage device is any piece of hardware that is capable ofstoring information, such as, for example, without limitation, at leastone of data, program code in functional form, or other suitableinformation either on a temporary basis, a permanent basis, or both on atemporary basis and a permanent basis. Storage devices 816 may also bereferred to as computer-readable storage devices in these illustrativeexamples. Memory 806, in these examples, can be, for example, arandom-access memory or any other suitable volatile or non-volatilestorage device. Persistent storage 808 may take various forms, dependingon the particular implementation.

For example, persistent storage 808 may contain one or more componentsor devices. For example, persistent storage 808 can be a hard drive, asolid-state drive (SSD), a flash memory, a rewritable optical disk, arewritable magnetic tape, or some combination of the above. The mediaused by persistent storage 808 also can be removable. For example, aremovable hard drive can be used for persistent storage 808.

Communications unit 810, in these illustrative examples, provides forcommunications with other data processing systems or devices. In theseillustrative examples, communications unit 810 is a network interfacecard.

Input/output unit 812 allows for input and output of data with otherdevices that can be connected to data processing system 800. Forexample, input/output unit 812 may provide a connection for user inputthrough at least one of a keyboard, a mouse, or some other suitableinput device. Further, input/output unit 812 may send output to aprinter. Display 814 provides a mechanism to display information to auser.

Instructions for at least one of the operating system, applications, orprograms can be located in storage devices 816, which are incommunication with processor unit 804 through communications framework802. The processes of the different embodiments can be performed byprocessor unit 804 using computer-implemented instructions, which may belocated in a memory, such as memory 806.

These instructions are referred to as program code, computer usableprogram code, or computer-readable program code that can be read andexecuted by a processor in processor unit 804. The program code in thedifferent embodiments can be embodied on different physical orcomputer-readable storage media, such as memory 806 or persistentstorage 808.

Program code 818 is located in a functional form on computer-readablemedia 820 that is selectively removable and can be loaded onto ortransferred to data processing system 800 for execution by processorunit 804. Program code 818 and computer-readable media 820 form computerprogram product 822 in these illustrative examples. In the illustrativeexample, computer-readable media 820 is computer-readable storage media824.

In these illustrative examples, computer-readable storage media 824 is aphysical or tangible storage device used to store program code 818rather than a medium that propagates or transmits program code 818.

Alternatively, program code 818 can be transferred to data processingsystem 800 using a computer-readable signal media. The computer-readablesignal media can be, for example, a propagated data signal containingprogram code 818. For example, the computer-readable signal media can beat least one of an electromagnetic signal, an optical signal, or anyother suitable type of signal. These signals can be transmitted overconnections, such as wireless connections, optical fiber cable, coaxialcable, a wire, or any other suitable type of connection.

The different components illustrated for data processing system 800 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments can be implemented. In some illustrative examples,one or more of the components may be incorporated in or otherwise form aportion of, another component. For example, memory 806, or portionsthereof, may be incorporated in processor unit 804 in some illustrativeexamples. The different illustrative embodiments can be implemented in adata processing system including components in addition to or in placeof those illustrated for data processing system 800. Other componentsshown in FIG. 8 can be varied from the illustrative examples shown. Thedifferent embodiments can be implemented using any hardware device orsystem capable of running program code 818.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiment. The terminology used herein was chosen to best explain theprinciples of the embodiment, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed here.

What is claimed is:
 1. A computer-implemented method of classifying aconcept of a data item, comprising: receiving a data item label for thedata item; analyzing the data item label using a natural languageprocessing tool to generate additional lexical information for the dataitem label; building a search query using the additional lexicalinformation for the data item label; using the search query to search aclue concept index to produce a search result, wherein the clue conceptindex comprises clue concept records that identify clues for concepts,and wherein the search result identifies clue concept records from theclue concept index that match the search query; and selecting a conceptfrom the clue concept records identified in the search result as theconcept for the data item.
 2. The computer-implemented method of claim1, wherein: the additional lexical information for the data item labelcomprises tokens, wherein each token in the tokens comprises text fromthe data item label and a lemma of the text from the data item label;and wherein building the search query comprises building the searchquery using the text and lemma from each token in the tokens for thedata item label.
 3. The computer-implemented method of claim 1, whereinthe search result comprises a relevance score for each clue conceptrecord identified in the search result, and further comprising:modifying the relevance score for each clue concept record identified inthe search result based on which field in the clue concept record ismatched to the search query in the search to produce a boosted relevancescore for each clue concept record identified in the search result; andusing the boosted relevance score to select a concept from the clueconcept records identified in the search result as the concept for thedata item.
 4. The computer-implemented method of claim 3 furthercomprising: determining a weighted relevance score for each clue conceptrecord identified in the search result by weighting the boostedrelevance score for each clue concept record identified in the searchresult with a weight determined by a scoring algorithm; and selectingconcepts in the clue concept record with the highest weighted relevancescore as concepts for the data item.
 5. The computer-implemented methodof claim 4, wherein weights determined by the scoring algorithm aretrained by a machine learning model.
 6. The computer-implemented methodof claim 1, comprising: identifying a language of the data item label;determining whether the language of the data item label is English; inresponse to a determination that the language of the data item label isnot English, using the search query to search a language-specific clueconcept index for the language of the data item label and an Englishclue concept index; in response to a determination that the language ofthe data item label is English, using the search query to search only anEnglish clue concept index.
 7. The computer implemented method of claim1 further comprising, generating the clue concept index by: receivingconcept labels for concepts; automatically generating lexical clues fromthe concept labels; analyzing the lexical clues using a natural languageprocessing tool to generate additional lexical information for theconcepts; generating the clue concept records using the lexical cluesand additional lexical information as clues for the concepts; andincorporating the clue concept records in the clue concept index.
 8. Asystem for classifying a concept of a data item, comprising a dataprocessing system configured to: receive a data item label for the dataitem; analyze the data item label using a natural language processingtool to generate additional lexical information for the data item label;build a search query using the additional lexical information for thedata item label; use the search query to search a clue concept index toproduce a search result, wherein the clue concept index comprises clueconcept records that identify clues for concepts, and wherein the searchresult identifies clue concept records from the clue concept index thatmatch the search query; and select a concept from the clue conceptrecords identified in the search result as the concept for the dataitem.
 9. The system of claim 8, wherein: the additional lexicalinformation for the data item label comprises tokens, wherein each tokenin the tokens comprises text from the data item label and a lemma of thetext from the data item label; and wherein the data processing system isconfigured to build the search query using the text and lemma from eachtoken in the tokens for the data item label.
 10. The system of claim 8,wherein the search result comprises a relevance score for each clueconcept record identified in the search result, and wherein the dataprocessing system is further configured to: modify the relevance scorefor each clue concept record identified in the search result based onwhich field in the clue concept record is matched to the search query inthe search to produce a boosted relevance score for each clue conceptrecord identified in the search result; and use the boosted relevancescore to select the concept from the clue concept records identified inthe search result as the concept for the data item.
 11. The system ofclaim 10, wherein the data processing system is further configured to:determine a weighted relevance score for each clue concept recordidentified in the search result by weighting the boosted relevance scorefor each clue concept record identified in the search result with aweight determined by a scoring algorithm; and select concepts in theclue concept record with the highest weighted relevance score asconcepts for the data item.
 12. The system of claim 11, wherein weightsdetermined by the scoring algorithm are trained by a machine learningmodel.
 13. The system of claim 8, wherein the data processing system isconfigured to: identify a language of the data item label; determinewhether the language of the data item label is English; in response to adetermination that the language of the data item label is not English,use the search query to search a language-specific clue concept indexfor the language of the data item label and an English clue conceptindex; in response to a determination that the language of the data itemlabel is English, use the search query to search only an English clueconcept index.
 14. The system of claim 8, wherein the data processingsystem is further configured to generate the clue concept index by:receiving concept labels for concepts; automatically generating lexicalclues from the concept labels; analyzing the lexical clues using anatural language processing tool to generate additional lexicalinformation for the concepts; generating the clue concept records usingthe lexical clues and additional lexical information as clues for theconcepts; and incorporating the clue concept records in the clue conceptindex.
 15. A computer program product for classifying a concept of adata item, the computer program product comprising a computer readablestorage medium having program instructions embodied therewith, theprogram instructions executable by a device to cause the device to:receive a data item label for the data item; analyze the data item labelusing a natural language processing tool to generate additional lexicalinformation for the data item label; build a search query using theadditional lexical information for the data item label; use the searchquery to search a clue concept index to produce a search result, whereinthe clue concept index comprises clue concept records that identifyclues for concepts, and wherein the search result identifies clueconcept records from the clue concept index that match the search query;and select a concept from the clue concept records identified in thesearch result as the concept for the data item.
 16. The computer programproduct of claim 15, wherein: the additional lexical information for thedata item label comprises tokens, wherein each token in the tokenscomprises text from the data item label and a lemma of the text from thedata item label; and wherein the program instructions are executable bythe device to cause the device to build the search query using the textand lemma from each token in the tokens for the data item label.
 17. Thecomputer program product of claim 15, wherein the search resultcomprises a relevance score for each clue concept record identified inthe search result, and wherein the program instructions are executableby the device to cause the device to: modify the relevance score foreach clue concept record identified in the search result based on whichfield in the clue concept record is matched to the search query in thesearch to produce a boosted relevance score for each clue concept recordidentified in the search result; and use the boosted relevance score toselect the concept from the clue concept records identified in thesearch result as the concept for the data item.
 18. The computer programproduct of claim 17, wherein the program instructions are executable bythe device to cause the device to: determine a weighted relevance scorefor each clue concept record identified in the search result byweighting the boosted relevance score for each clue concept recordidentified in the search result with a weight determined by a scoringalgorithm; and select concepts in the clue concept record with thehighest weighted relevance score as concepts for the data item.
 19. Thecomputer program product of claim 15, wherein the program instructionsare executable by the device to cause the device to: identify a languageof the data item label; determine whether the language of the data itemlabel is English; in response to a determination that the language ofthe data item label is not English, use the search query to search alanguage-specific clue concept index for the language of the data itemlabel and an English clue concept index; in response to a determinationthat the language of the data item label is English, use the searchquery to search only an English clue concept index.
 20. The computerprogram product of claim 15, wherein the program instructions areexecutable by the device to cause the device to generate the clueconcept index by: receiving concept labels for concepts; automaticallygenerating lexical clues from the concept labels; analyzing the lexicalclues using a natural language processing tool to generate additionallexical information for the concepts; generating the clue conceptrecords using the lexical clues and additional lexical information asclues for the concepts; and incorporating the clue concept records inthe clue concept index.